In this paper, we have proposed a method of fusing LiDAR depth measurements into an existing indirect visual SLAM algorithm, namely ORB-SLAM3 [1]}. We compare two methods to generate pixel-wise depth maps for each camera image. For upsampling the LiDAR point cloud, we used an inverse dilation with a 5x5 pixel-sized diamond kernel and a supervised deep learning-based method based on ENet [2]}. While the deep learning-based depth maps had to be created offline before running the SLAM, the dilation was directly included in the ORB SLAM3 as RGB-L mode. The code will be available as open-source software.
